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2025 Sample Associate-Developer-Apache-Spark-3.5 Questions Answers Free PDF | High Pass-Rate Associate-Developer-Apache-Spark-3.5 Exam Blueprint: Databricks Certified Associate Developer for Apache Spark 3.5 - Python

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Databricks Certified Associate Developer for Apache Spark 3.5 - Python Sample Questions (Q14-Q19):

NEW QUESTION # 14
A DataFramedfhas columnsname,age, andsalary. The developer needs to sort the DataFrame byagein ascending order andsalaryin descending order.
Which code snippet meets the requirement of the developer?

A. df.sort("age", "salary", ascending=[False, True]).show()
B. df.orderBy("age", "salary", ascending=[True, False]).show()
C. df.orderBy(col("age").asc(), col("salary").asc()).show()
D. df.sort("age", "salary", ascending=[True, True]).show()

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To sort a PySpark DataFrame by multiple columns with mixed sort directions, the correct usage is:
python
CopyEdit
df.orderBy("age","salary", ascending=[True,False])
agewill be sorted in ascending order
salarywill be sorted in descending order
TheorderBy()andsort()methods in PySpark accept a list of booleans to specify the sort direction for each column.
Documentation Reference:PySpark API - DataFrame.orderBy

NEW QUESTION # 15
An MLOps engineer is building a Pandas UDF that applies a language model that translates English strings into Spanish. The initial code is loading the model on every call to the UDF, which is hurting the performance of the data pipeline.
The initial code is:

def in_spanish_inner(df: pd.Series) -> pd.Series:
model = get_translation_model(target_lang='es')
return df.apply(model)
in_spanish = sf.pandas_udf(in_spanish_inner, StringType())
How can the MLOps engineer change this code to reduce how many times the language model is loaded?

A. Convert the Pandas UDF from a Series # Series UDF to an Iterator[Series] # Iterator[Series] UDF
B. Run thein_spanish_inner()function in amapInPandas()function call
C. Convert the Pandas UDF from a Series # Series UDF to a Series # Scalar UDF
D. Convert the Pandas UDF to a PySpark UDF

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The provided code defines a Pandas UDF of type Series-to-Series, where a new instance of the language modelis created on each call, which happens per batch. This is inefficient and results in significant overhead due to repeated model initialization.
To reduce the frequency of model loading, the engineer should convert the UDF to an iterator-based Pandas UDF (Iterator[pd.Series] -> Iterator[pd.Series]). This allows the model to be loaded once per executor and reused across multiple batches, rather than once per call.
From the official Databricks documentation:
"Iterator of Series to Iterator of Series UDFs are useful when the UDF initialization is expensive... For example, loading a ML model once per executor rather than once per row/batch."
- Databricks Official Docs: Pandas UDFs
Correct implementation looks like:
python
CopyEdit
@pandas_udf("string")
def translate_udf(batch_iter: Iterator[pd.Series]) -> Iterator[pd.Series]:
model = get_translation_model(target_lang='es')
for batch in batch_iter:
yield batch.apply(model)
This refactor ensures theget_translation_model()is invoked once per executor process, not per batch, significantly improving pipeline performance.

NEW QUESTION # 16
A data engineer is building a Structured Streaming pipeline and wants the pipeline to recover from failures or intentional shutdowns by continuing where the pipeline left off.
How can this be achieved?

A. By configuring the optionrecoveryLocationduringwriteStream
B. By configuring the optioncheckpointLocationduringwriteStream
C. By configuring the optionrecoveryLocationduring the SparkSession initialization
D. By configuring the optioncheckpointLocationduringreadStream

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To enable a Structured Streaming query to recover from failures or intentional shutdowns, it is essential to specify thecheckpointLocationoption during thewriteStreamoperation. This checkpoint location stores the progress information of the streaming query, allowing it to resume from where it left off.
According to the Databricks documentation:
"You must specify thecheckpointLocationoption before you run a streaming query, as in the following example:
option("checkpointLocation", "/path/to/checkpoint/dir")
toTable("catalog.schema.table")
- Databricks Documentation: Structured Streaming checkpoints
By setting thecheckpointLocationduringwriteStream, Spark can maintain state information and ensure exactly- once processing semantics, which are crucial for reliable streaming applications.

NEW QUESTION # 17
A data engineer is working with a large JSON dataset containing order information. The dataset is stored in a distributed file system and needs to be loaded into a Spark DataFrame for analysis. The data engineer wants to ensure that the schema is correctly defined and that the data is read efficiently.
Which approach should the data scientist use to efficiently load the JSON data into a Spark DataFrame with a predefined schema?

A. Use spark.read.json() to load the data, then use DataFrame.printSchema() to view the inferred schema, and finally use DataFrame.cast() to modify column types.
B. Define a StructType schema and use spark.read.schema(predefinedSchema).json() to load the data.
C. Use spark.read.json() with the inferSchema option set to true
D. Use spark.read.format("json").load() and then use DataFrame.withColumn() to cast each column to the desired data type.

Answer: B

Explanation:
The most efficient and correct approach is to define a schema using StructType and pass it tospark.read.
schema(...).
This avoids schema inference overhead and ensures proper data types are enforced during read.
Example:
frompyspark.sql.typesimportStructType, StructField, StringType, DoubleType schema = StructType([ StructField("order_id", StringType(),True), StructField("amount", DoubleType(),True),
])
df = spark.read.schema(schema).json("path/to/json")
- Source:Databricks Guide - Read JSON with predefined schema

NEW QUESTION # 18
A Spark application suffers from too many small tasks due to excessive partitioning. How can this be fixed without a full shuffle?
Options:

A. Use the coalesce() transformation with a lower number of partitions
B. Use the distinct() transformation to combine similar partitions
C. Use the repartition() transformation with a lower number of partitions
D. Use the sortBy() transformation to reorganize the data

Answer: A

Explanation:
coalesce(n) reduces the number of partitions without triggering a full shuffle, unlike repartition().
This is ideal when reducing partition count, especially during write operations.
Reference:Spark API - coalesce

NEW QUESTION # 19
......

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